Vemishetty Naresh, Jadhav Pranit, Adapa Bhagyaraja, Acharyya Amit, Pachamuthu Rajalakshmi, Naik Ganesh R
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:5082-5. doi: 10.1109/EMBC.2015.7319534.
This paper introduces a dual-mode low complex on-chip methodology for processing of ECG (Electrocardiogram) and EEG (Electroencephalography) signals, wherein based on the input switch the architecture can be dynamically configured to operate either as an ECG bio-marker or EEG signal de-noising system. In both the modes the signal processing technique depends on the output of the DWT (Discrete Wavelet Transform), hence a low complex methodology has been developed in which both ECG and EEG processing blocks sharing the same DWT block resulting in low area and low power consumption. The integrated ECG and EEG methodology has been implemented in Matlab, for verifying the ECG processing block the ECG database is taken from MIT-BIH PTBDB and IITH DB, similarly for EEG processing block the EEG signals are taken from PhysioNet database. The outcome of methodology in Matlab is equal to the results obtained from individual ECG and EEG blocks.
本文介绍了一种用于处理心电图（ECG）和脑电图（EEG）信号的双模式低复杂度片上方法，其中基于输入开关，该架构可以动态配置为作为心电图生物标志物或脑电图信号去噪系统运行。在两种模式下，信号处理技术都依赖于离散小波变换（DWT）的输出，因此开发了一种低复杂度方法，其中心电图和脑电图处理模块共享同一个DWT模块，从而实现低面积和低功耗。集成的心电图和脑电图方法已在Matlab中实现，为验证心电图处理模块，心电图数据库取自麻省理工学院-比尔汉姆心律失常数据库（MIT-BIH PTBDB）和印度理工学院海得拉巴分校数据库（IITH DB），类似地，为验证脑电图处理模块，脑电图信号取自生理网数据库（PhysioNet database）。Matlab中该方法的结果与从单独的心电图和脑电图模块获得的结果相同。
